Mobile communications is experiencing enormous growth, thereby requiring proper planning, expanding, operating and optimizing of mobile communications networks. For example, in a public safety network having one or more base stations in radio communication with land mobile radios (LMRs), both vehicular and handheld, operated by public safety personnel, such as first responders, too few stations may result in spotty or unreliable radio coverage, whereas too many stations are redundant and expensive. A radio signal experiences path loss during propagation between a mobile radio and a network transceiver at a station. Path loss is the attenuation or reduction in power of the propagated radio signal and is due to myriad variable factors, e.g., the spreading of the radio signal over the distance between the radio and the station, the height and location of antennas on the radio and the station, the terrain profile (hilly, mountainous, flat, etc.), the environment (urban, suburban, rural, open, forested, sea, etc.), and so forth. For example, the radio signal could be at least partially absorbed, reflected, or diffracted by trees, buildings, etc. in its path of propagation. Similarly, in a telephone network having one or more cell towers in radio communication with handheld, mobile phones having built-in radio transceivers, too few towers can be as problematic as too many towers, and the radio signal similarly experiences path loss during propagation between a mobile phone and a network transceiver at a tower.
Determining or calculating the path loss (usually expressed in dB) is known as propagation prediction, and various prediction models, tools, systems, and methods have been employed for network planning and optimization. One popular empirical model is described by Okumura et al. in “Field Strength and its Variability in VHF and UHF Land-Mobile Radio Service,” Rev. Elec. Commun. Lab., vol. 16, no. 3, 1968, pp. 825-873 (the “Okumura model”), in which field strength versus distance for various terrains, environments, and antenna heights are predicted. Measurement test data are often used to fine tune the Okumura model, as well as other models, based on a comparison of predicted versus measured signal strength. Yet, existing tuning methods that are based solely on signal strength still leaves uncertainty in the accuracy of the propagation prediction as it relates to network performance.
Accordingly, there is a need to optimize any empirical propagation prediction model to increase the accuracy of the propagation prediction in mobile communications networks.
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